# \[ANN\] OnlineStatsChains.jl - Chain OnlineStats computations in DAG pipelines ⚠️ Experimental

**URL:** <https://discourse.julialang.org/t/ann-onlinestatschains-jl-chain-onlinestats-computations-in-dag-pipelines-experimental/132849>\
**Category:** Package Announcements\
**Created:** [October 3, 2025, 11:33am UTC](https://discourse.julialang.org/t/ann-onlinestatschains-jl-chain-onlinestats-computations-in-dag-pipelines-experimental/132849 "2025-10-03T11:33:32Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![FemtoTrader](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/femtotrader/32/309_2.png) [@FemtoTrader](https://discourse.julialang.org/u/FemtoTrader)\
**Post date:** [October 3, 2025, 11:33am UTC](https://discourse.julialang.org/t/ann-onlinestatschains-jl-chain-onlinestats-computations-in-dag-pipelines-experimental/132849/1 "2025-10-03T11:33:32Z")

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Hi,

I’m excited to announce **OnlineStatsChains.jl** , a package that enables you to chain [OnlineStats](https://github.com/joshday/OnlineStats.jl) computations in a Directed Acyclic Graph (DAG) structure with automatic value propagation.

**⚠ Important: This is a highly experimental package.** It was created as an exploration of AI-assisted development and may not be suitable for production use without thorough testing and validation for your specific use case.

## 🎯 What it does

OnlineStatsChains.jl allows you to build computational pipelines where statistics are automatically updated as data flows through a graph. Perfect for streaming data processing, incremental analytics, and complex statistical workflows.

This package addresses the feature request in [OnlineStats.jl#272](https://github.com/joshday/OnlineStats.jl/issues/272) for chaining OnlineStats computations.

## ✨ Key Features

- **📊 DAG Construction** : Build computational graphs with automatic cycle detection
- **⚡ Three Evaluation Strategies** :
  - _Eager_ (default): Immediate propagation when `fit!()` is called
  - _Lazy_: Deferred computation until `value()` is requested
  - _Partial_: Optimized propagation for affected subgraphs only

- **🔀 Multi-Input Nodes** : Support for fan-in and fan-out patterns
- **📈 Batch & Streaming** : Process data element-by-element or in batches
- **🔧 Type-Safe** : Works with any `OnlineStat` from OnlineStatsBase.jl

## 📝 Quick Example

```julia
using OnlineStatsChains
using OnlineStatsBase

# Create a computational DAG
dag = StatDAG()

# Add nodes
add_node!(dag, :source, Mean())
add_node!(dag, :variance, Variance())

# Connect nodes
connect!(dag, :source, :variance)

# Fit data (propagates automatically)
fit!(dag, :source => [1.0, 2.0, 3.0, 4.0, 5.0])

# Get results
println("Mean: ", value(dag, :source)) # 3.0
println("Variance: ", value(dag, :variance))

```

## 🤖 AI-Generated Package Notice

**This package was entirely generated using Claude Code (Anthropic’s AI coding tool).**

I wanted to experiment with AI-assisted package development and see what could be achieved. The entire codebase, tests, and documentation were created through an iterative process with Claude Code.

### Development Methodology

The development process leveraged:

- **EARS (Easy Approach to Requirements Syntax)**: Requirements were written using structured natural language patterns to ensure clarity and completeness
- **BDD (Behavior-Driven Development)**: Test scenarios were written in Given-When-Then format to describe expected behavior before implementation
- **Iterative refinement** : Claude Code generated code, tests, and documentation through multiple iterations based on these specifications

### Why share this?

- **Transparency** : Users should know the development process and experimental nature
- **Experimentation** : Testing the boundaries of AI-assisted package development in Julia
- **Community feedback** : Learning what works (and what doesn’t) in AI-generated code
- **Research interest** : Understanding how AI tools can contribute to the Julia ecosystem

### What this means for users

**This package should be considered experimental software.** While it includes comprehensive tests and documentation, I strongly encourage you to:

- ✅ Review the code for your specific use case
- ✅ Add application-specific tests
- ✅ Report any issues found
- ✅ Verify behavior matches your requirements
- ⚠ **Do not use in production without extensive validation**
- ⚠ **Expect potential breaking changes as the API stabilizes**

This is part of exploring how AI tools can accelerate Julia package development while maintaining quality standards. I’m particularly interested in feedback on both the package functionality and this development approach. Consider this a proof-of-concept and learning experiment rather than a battle-tested library.

## 🚀 Installation

```julia
# Once registered in General
using Pkg
Pkg.add("OnlineStatsChains")

# Or from GitHub
Pkg.add(url="https://github.com/femtotrader/OnlineStatsChains.jl")

```

## 💡 Use Cases

- **Streaming analytics** : Update multiple statistics as data arrives
- **Pipeline processing** : Chain transformations and aggregations
- **Complex workflows** : Build sophisticated statistical pipelines with dependencies
- **Incremental computation** : Efficiently recompute only affected parts of the graph

## 🔍 Related Packages

While there are several excellent Julia packages for DAG-based computation (like Dagger.jl for parallel execution, ReactiveGraphs.jl for reactive programming, or DirectedAcyclicGraphs.jl for general DAG infrastructure), OnlineStatsChains.jl focuses specifically on:

- **Incremental statistics** : Built specifically for OnlineStats.jl’s streaming statistics
- **Automatic propagation** : Statistics update automatically as data flows through the graph
- **Multiple evaluation strategies** : Eager/Lazy/Partial modes optimized for statistical workflows
- **Lightweight** : Designed for statistical pipelines rather than general-purpose parallel computation

If you need heavy parallel computation across multiple machines, Dagger.jl is likely more appropriate. If you’re building reactive data processing applications, check out ReactiveGraphs.jl. OnlineStatsChains.jl is best suited for chaining statistical computations in an incremental manner.

## 📚 Resources

- **Documentation** : [Home · OnlineStatsChains.jl](https://femtotrader.github.io/OnlineStatsChains.jl/)
- **Repository** : [GitHub - femtotrader/OnlineStatsChains.jl: A Julia package for chaining OnlineStats computations in a Directed Acyclic Graph (DAG) structure with automatic value propagation](https://github.com/femtotrader/OnlineStatsChains.jl)
- **Issues** : [GitHub · Where software is built](https://github.com/femtotrader/OnlineStatsChains.jl/issues)

## 🙏 Acknowledgments

Built on top of the excellent [OnlineStats.jl](https://github.com/joshday/OnlineStats.jl) ecosystem by @joshday.

* * *

Looking forward to your feedback, suggestions, and hearing about how you might use this! Questions and contributions are very welcome.

## 🔮 Potential Extensions

The current implementation provides core DAG functionality for chaining OnlineStats. Some areas that could potentially be explored in the future include parallel execution of independent branches, integration with streaming data sources, or more sophisticated graph manipulation capabilities.

However, I’m particularly interested in hearing what features would be most valuable to actual users. What would make this package more useful for your workflows?

---

<div class="post-metadata">

**Author:** ![FemtoTrader](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/femtotrader/32/309_2.png) [@FemtoTrader](https://discourse.julialang.org/u/FemtoTrader)\
**Post date:** [January 5, 2026, 4:58pm UTC](https://discourse.julialang.org/t/ann-onlinestatschains-jl-chain-onlinestats-computations-in-dag-pipelines-experimental/132849/2 "2026-01-05T16:58:39Z")

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Hi,

I’m excited to share that [GitHub - femtotrader/OnlineTechnicalIndicators.jl: Julia Incremental Technical Analysis Indicators (inspired by talipp)](https://github.com/femtotrader/OnlineTechnicalIndicators.jl) now uses StatDAG in v0.2.0 and above from [GitHub - femtotrader/OnlineStatsChains.jl: A Julia package for chaining OnlineStats computations in a Directed Acyclic Graph (DAG) structure with automatic value propagation](https://github.com/femtotrader/OnlineStatsChains.jl) to compose complex technical analysis indicators!

## What’s new?

OnlineTechnicalIndicators.jl implements technical analysis indicators using an incremental/online approach. The latest update introduces StatDAG (from OnlineStatsChains.jl) for composing indicators that depend on multiple sub-indicators.

## Why StatDAG?

Complex indicators like DEMA (Double Exponential Moving Average) require chaining multiple EMA calculations together. Previously, managing data flow between sub-indicators required manual state management. With StatDAG:

- 📊 Automatic data propagation - Data flows through the DAG automatically during each fit!() call

- 🔗 Filtered edges - Downstream indicators only receive valid (non-missing) values

- 🎯 Clean composition - Complex indicators are built declaratively from simpler components

Example: DEMA via StatDAG

DEMA is computed as 2 \* EMA₁ - EMA₂, where EMA₂ receives the output of EMA₁:

```
using OnlineTechnicalIndicators.Indicators: DEMA, fit!, value
dema = DEMA{Float64}(period=10)
prices = [100.0, 101.5, 102.3, 101.8, 103.2, 104.1]
for price in prices
    fit!(dema, price)
    println("DEMA: ", value(dema))
end

```

Internally, StatDAG manages the data flow:

- `:ema1` receives raw input data
- `:ema2` receives filtered output from :ema1 (only when not missing)
- Final value: `2 * ema1 - ema2`

Key Benefits

- ⚡ O(1) memory - Indicators process data one observation at a time

- 🔄 Streaming-friendly - Perfect for real-time market data

- 🏗 Extensible - Easy to create new composed indicators using the StatDAG pattern

Resources

- Documentation: [Data Flow · OnlineTechnicalIndicators.jl](https://femtotrader.github.io/OnlineTechnicalIndicators.jl/dev/dataflow/)

- GitHub: [GitHub - femtotrader/OnlineTechnicalIndicators.jl: Julia Incremental Technical Analysis Indicators (inspired by talipp)](https://github.com/femtotrader/OnlineTechnicalIndicators.jl)

- OnlineStatsChains.jl: [GitHub - femtotrader/OnlineStatsChains.jl: A Julia package for chaining OnlineStats computations in a Directed Acyclic Graph (DAG) structure with automatic value propagation](https://github.com/femtotrader/OnlineStatsChains.jl)

## Installation

```
using Pkg
Pkg.add("OnlineTechnicalIndicators")

```

Feedback Welcome!

If you’re working with streaming financial data or technical analysis in Julia, I’d love to hear your feedback. Issues and PRs are welcome on GitHub!